The present invention relates to the field of computer vision and to medical imaging analysis in particular.
Image segmentation is fundamental to medical imaging analysis and therefore actively studied, while numerous approaches exist. Recent trends focus on fully automatic segmentation frameworks, being much faster than manual annotation, less biased and repeatable. Usually, the required workload for processing and analyzing large datasets is far behind the ability of a human rater. Moreover, the computational advancements of the machine in cases that require modality fusion or three-dimensional (3D) visualization cannot be competed even by an expert. Nevertheless, as the outcome of the image analysis process might have critical implications on the patient recuperation prospects the expertise of a clinician must be considered.
Interactive segmentation (IS) approaches may be classified based on the form and the type of input provided by the user as well as the underlying segmentation framework (see: F. Zhao, and X. Xie. An overview of interactive medical image segmentation. Annals of the BMVA, 7:1-22, 2013). The pioneering IS work, which led to the development of the live wire technique or intelligent scissors (independently suggested by L. Dice. Measure of the amount of ecological association between species. Ecology, 26(3):297-302, 1945 and E. N. Mortensen and W. A. Barrett. Interactive segmentation with intelligent scissors. Graph. Models and Image Proc., 60(5): 349-384, 1998) is based on the image edge map. The shortest paths between the user's mouse clicks, calculated by the Dijkstra algorithm form the contour of the region of interest (ROI). In the united snakes (see: J. Liang, T. McInerney, and D. Terzopoulos. United snakes. 10(2):215-233, 2006), which relies on a classical active contour framework known as snakes (see: M. Kass, A. P. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, 1(4):321-331, Jan. 1988), the user ‘plants’ anchors or seed points along the desired boundary, providing guidance for the segmentation.
Pointing device scribbles seem to be the most common form of user interaction. Marked regions (e.g., by pointing device scribbles) may provide information about the ROI and the background intensity distributions. A well known IS approach is the GrabCut technique (see: C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. SIGGRAPH, 2004) which is based on the graph-cut (see: Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 23(11):1222-1239, 2001). Representing the image pixels by nodes in a graph, the graph-cut addresses a foreground-background image segmentation by solving a min-cut, max-flow problem. The user's annotated regions are assigned to either the source or the sink of the graph. In a recent paper by C. Nieuwenhuis and D. Cremers. Spatially varying color distributions for interactive multilabel segmentation. PAMI, 35(5): 1234-1247, 2013, marked user regions, via mouse scribbles were used for gathering spatially varying color statistics for multi-label segmentation. Level-set based segmentation framework with User Interface (UI) which are designed for medical images were suggested in Y. Gao, R. Kikinis, S. Bouix, M. E. Shenton, and A. Tannenbaum. A 3D interactive multi-object segmentation tool using local robust statistics driven active contours. Medical image analysis, 16(6):1216-1227, 2012, and P. Karasev, I. Kolesov, K. Fritscher, P. Vela, P. Mitchell, and A. Tannenbaum. Interactive medical image segmentation using PDE control of active contours. 2013. Other recent IS techniques include J. S. Prassni, T. Ropinski, and K Hinrichs. Uncertainty-aware guided volume segmentation. Visualization and Computer Graphics, 16(6):1358-1365, 2010; W. Yang, J. Cai, J. Zheng, and J. Luo. User-friendly interactive image segmentation through unified combinatorial user inputs. TMI, 19(9):2470-2479, 2010; L. Paulhac, J-Y. Ramel, and T. Renard. Interactive segmentation of 3D images using a region adjacency graph representation. In Image Analysis and Recognition, pages 354-364. 2011 and K. McGuinness and N. E. OConnor. Toward automated evaluation of interactive segmentation. CVIU, 115(6):868-884, 2011.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.